M. Unser

We introduce a Hilbert-space framework, inspired by wavelet theory, that provides an exact link between the traditional—discrete and analog—formulations of signal processing. In contrast to Shannon's sampling theory, our approach uses basis functions that are compactly supported and therefore better suited for numerical computations. The underlying continuous-time signal model is of exponential spline type (with rational transfer function); this family of functions has the advantage of being closed under the basic signal-processing operations: differentiation, continuous-time convolution, and modulation. A key point of the method is that it allows an exact implementation of continuous-time operators by simple processing in the discrete domain, provided that one updates the basis functions appropriately. The framework is ideally suited for hybrid signal processing because it can jointly represent the effect of the various (analog or digital) components of the system. This point will be illustrated with the design of hybrid systems for improved A-to-D and D-to-A conversion. On the more fundamental front, the proposed formulation sheds new light on the striking parallel that exists between the basic analog and discrete operators in the classical theory of linear systems.